


'''simple docstring'''







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase     :  Dict											=    logging.get_logger(__name__)  # pylint: disable=invalid-name






def       lowercase					(_A       ):







       """simple docstring"""







       warnings.warn(
           'The preprocess method is deprecated and will be removed in a future version. Please'
           ' use VaeImageProcessor.preprocess instead' ,    _A ,    )
       if isinstance(_A ,    torch.Tensor       ):
              return image
       elif isinstance(_A ,    PIL.Image.Image       ):
              _lowerCAmelCase     :				Dict       =							[image]

       if isinstance(image[0] ,    PIL.Image.Image       ):
              _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							image[0].size
              _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

              _lowerCAmelCase     :				Any       =							[np.array(i.resize((w, h) ,    resample=PIL_INTERPOLATION['lanczos']       )       )[None, :] for i in image]
              _lowerCAmelCase     :				Tuple       =							np.concatenate(_A ,    axis=0       )
              _lowerCAmelCase     :				Union[str, Any]       =							np.array(_A       ).astype(np.floataa       ) / 255.0
              _lowerCAmelCase     :				Tuple       =							image.transpose(0 ,    3 ,    1 ,    2       )
              _lowerCAmelCase     :				str       =							2.0 * image - 1.0
              _lowerCAmelCase     :				List[Any]       =							torch.from_numpy(_A       )
       elif isinstance(image[0] ,    torch.Tensor       ):
              _lowerCAmelCase     :				Union[str, Any]       =							torch.cat(_A ,    dim=0       )
       return image






def       lowercase					(_A       ):







       """simple docstring"""







       if isinstance(_A ,    torch.Tensor       ):
              return mask
       elif isinstance(_A ,    PIL.Image.Image       ):
              _lowerCAmelCase     :				Dict       =							[mask]

       if isinstance(mask[0] ,    PIL.Image.Image       ):
              _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							mask[0].size
              _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
              _lowerCAmelCase     :				str       =							[np.array(m.convert('L'       ).resize((w, h) ,    resample=PIL_INTERPOLATION['nearest']       )       )[None, :] for m in mask]
              _lowerCAmelCase     :				Optional[int]       =							np.concatenate(_A ,    axis=0       )
              _lowerCAmelCase     :				List[str]       =							mask.astype(np.floataa       ) / 255.0
              _lowerCAmelCase     :				List[Any]       =							0
              _lowerCAmelCase     :				Optional[int]       =							1
              _lowerCAmelCase     :				int       =							torch.from_numpy(_A       )
       elif isinstance(mask[0] ,    torch.Tensor       ):
              _lowerCAmelCase     :				List[str]       =							torch.cat(_A ,    dim=0       )
       return mask
class 						UpperCamelCase__       (			_UpperCAmelCase						):



     """simple docstring"""






     __magic_name__           =     42
     __magic_name__           =     42







     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            super().__init__()
            self.register_modules(unet=__UpperCamelCase   ,						scheduler=__UpperCamelCase )







     @torch.no_grad()
     def __call__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ = 250   ,						snake_case__ = 0.0   ,						snake_case__ = 10   ,						snake_case__ = 10   ,						snake_case__ = None   ,						snake_case__ = "pil"   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							image

            _lowerCAmelCase     :				List[str]       =							_preprocess_image(__UpperCamelCase )
            _lowerCAmelCase     :				Optional[Any]       =							original_image.to(device=self.device   ,						dtype=self.unet.dtype )
            _lowerCAmelCase     :				List[str]       =							_preprocess_mask(__UpperCamelCase )
            _lowerCAmelCase     :				Any       =							mask_image.to(device=self.device   ,						dtype=self.unet.dtype )

            _lowerCAmelCase     :				List[str]       =							original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__UpperCamelCase   ,						__UpperCamelCase ) and len(__UpperCamelCase ) != batch_size:
                   raise ValueError(
                       F'You have passed a list of generators of length {len(__UpperCamelCase )}, but requested an effective batch'
                       F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )

            _lowerCAmelCase     :				Tuple       =							original_image.shape
            _lowerCAmelCase     :				Tuple       =							randn_tensor(__UpperCamelCase   ,						generator=__UpperCamelCase   ,						device=self.device   ,						dtype=self.unet.dtype )

            # set step values
            self.scheduler.set_timesteps(__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						self.device )
            _lowerCAmelCase     :				List[Any]       =							eta

            _lowerCAmelCase     :				str       =							self.scheduler.timesteps[0] + 1
            _lowerCAmelCase     :				List[Any]       =							generator[0] if isinstance(__UpperCamelCase   ,						__UpperCamelCase ) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                   if t < t_last:
                          # predict the noise residual
                          _lowerCAmelCase     :				int       =							self.unet(__UpperCamelCase   ,						__UpperCamelCase ).sample
                          # compute previous image: x_t -> x_t-1
                          _lowerCAmelCase     :				Optional[int]       =							self.scheduler.step(__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase ).prev_sample

                   else:
                          # compute the reverse: x_t-1 -> x_t
                          _lowerCAmelCase     :				int       =							self.scheduler.undo_step(__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase )
                   _lowerCAmelCase     :				Any       =							t

            _lowerCAmelCase     :				Dict       =							(image / 2 + 0.5).clamp(0   ,						1 )
            _lowerCAmelCase     :				List[Any]       =							image.cpu().permute(0   ,						2   ,						3   ,						1 ).numpy()
            if output_type == "pil":
                   _lowerCAmelCase     :				Optional[Any]       =							self.numpy_to_pil(__UpperCamelCase )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__UpperCamelCase )



"""simple docstring"""



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


a       :						int       		=		'''scheduler_config.json'''




class 		a_    (			_UpperCAmelCase						):
    a   :					List[Any]			     =					1
    a   :					Tuple			     =					2
    a   :					Dict			     =					3
    a   :					str			     =					4
    a   :					Optional[int]			     =					5
    a   :					Any			     =					6
    a   :					int			     =					7
    a   :					Any			     =					8
    a   :					List[Any]			     =					9
    a   :					Any			     =					10
    a   :					List[str]			     =					11
    a   :					Optional[int]			     =					12
    a   :					Any			     =					13
    a   :					Dict			     =					14







@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					torch.FloatTensor







class 		a_    :
    a   :					Dict			     =					SCHEDULER_CONFIG_NAME
    a   :					List[str]			     =					[]
    a   :					Optional[int]			     =					True







    @classmethod
    def   _snake_case       (      cls      :							List[str]  ,       __UpperCamelCase      :							Dict[str, Any] = None  ,       __UpperCamelCase      :							Optional[str] = None  ,       __UpperCamelCase      :							List[str]=False  ,       **__UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = cls.load_config(
            pretrained_model_name_or_path=__UpperCamelCase  ,       subfolder=__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       return_commit_hash=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        return cls.from_config(__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, os.PathLike]  ,       __UpperCamelCase      :							bool = False  ,       **__UpperCamelCase      :							List[str]  )		->Any:






        '''simple docstring'''






        self.save_config(save_directory=__UpperCamelCase  ,       push_to_hub=__UpperCamelCase  ,       **__UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        return self._get_compatibles()







    @classmethod
    def   _snake_case       (      cls      :							List[str]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = list(set([cls.__name__] + cls._compatibles  )  )
        _UpperCAmelCase        = importlib.import_module(__name__.split("""."""  )[0]  )
        _UpperCAmelCase        = [
            getattr(__UpperCamelCase  ,       __UpperCamelCase  ) for c in compatible_classes_str if hasattr(__UpperCamelCase  ,       __UpperCamelCase  )
        ]
        return compatible_classes